Generating static scenes

ABSTRACT

Implementations generally relate to generating static scenes. In some implementations, a method includes collecting photos associated with objects in at least one location. The method also includes collecting attention information associated with one or more of the objects. The method also includes generating an attention map based on the attention information. The method also includes generating a model of the at least one location based on the photos and the attention map.

BACKGROUND

Digital cameras have made taking photos easy and convenient, and various applications have made sharing photos easy and convenient. For example, some applications enable a person to instantly upload photos to a social network system as photos are captured. Some applications can combine multiple photos of a particular location in order to create a panoramic view of the location.

SUMMARY

Implementations generally relate to generating static scenes. In some implementations, a method includes collecting photos associated with objects in at least one location. The method also includes collecting attention information associated with one or more of the objects. The method also includes generating an attention map based on the attention information. The method also includes generating a model of the at least one location based on the photos and the attention map.

With further regard to the method, one or more of the photos are captured using one or more respective devices that are operable to track a gaze of a user. In some implementations, the attention information is based on tracking gazes of one or more users. In some implementations, the generating of the model includes combining the photos into a panorama of the at least one location. In some implementations, the generating of the model includes: identifying the objects in the attention map; selecting photos for each of the objects based on one or more predetermined selection criteria; and combining the photos into a panorama of the at least one location. In some implementations, the generating of the model includes: identifying the objects in the attention map; selecting photos for each of the objects based on one or more predetermined selection criteria, where the one or more predetermined selection criteria is based on one or more of resolution, quality, field of view, and level of attention; and combining the photos into a panorama of the at least one location. In some implementations, the generating of the model includes combining the photos into a panorama of the at least one location, where the combining includes: registering images of the objects in the photos; calibrating the images of the objects; and blending the images. In some implementations, the generating of the model includes combining the photos into a panorama of the at least one location, and where the method further includes iteratively replacing photos in the panorama with replacement photos based on one or more predetermined replacement criteria. In some implementations, the generating of the model includes combining the photos into a panorama of the at least one location, where the method further includes iteratively replacing photos in the panorama with replacement photos based on one or more predetermined replacement criteria, and where the replacement criteria may include one or more of quality and resolution. In some implementations, the generating of the model includes combining the photos into a panorama of the at least one location, and where the method further includes enabling a user to overlay one or more user-selected photos over one or more portions of the panorama.

In some implementations, a method includes collecting photos associated with objects in at least one location, where one or more of the photos are captured using one or more respective devices that are operable to track a gaze of a user. The method further includes collecting attention information associated with one or more of the objects, where the attention information is based on tracking gazes of one or more users. The method further includes generating an attention map based on the attention information. The method further includes generating a model of the at least one location based on the photos and the attention map, where the generating of the model includes combining the photos into a panorama of the at least one location, and where the method further includes iteratively replacing photos in the panorama with replacement photos based on one or more predetermined replacement criteria.

In some implementations, a system includes one or more processors, and logic encoded in one or more tangible media for execution by the one or more processors. When executed, the logic is operable to perform operations including: collecting photos associated with objects in at least one location; collecting attention information associated with one or more of the objects; generating an attention map based on the attention information; and generating a model of the at least one location based on the photos and the attention map.

With further regard to the system, one or more of the photos are captured using one or more respective devices that are operable to track a gaze of a user. In some implementations, the attention information is based on tracking gazes of one or more users. In some implementations, to generate the model, the logic when executed is further operable to perform operations including combining the photos into a panorama of the at least one location. In some implementations, to generate the model, the logic when executed is further operable to perform operations including: identifying the objects in the attention map; selecting photos for each of the objects based on one or more predetermined selection criteria; and combining the photos into a panorama of the at least one location. In some implementations, to generate the model, the logic when executed is further operable to perform operations including: identifying the objects in the attention map; selecting photos for each of the objects based on one or more predetermined selection criteria, where the one or more predetermined selection criteria is based on one or more of resolution, quality, field of view, and level of attention; and combining the photos into a panorama of the at least one location. In some implementations, to generate the model, the logic when executed is further operable to perform operations including combining the photos into a panorama of the at least one location, where the combining includes: registering images of the objects in the photos; calibrating the images of the objects; and blending the images. In some implementations, to generate the model, the logic when executed is further operable to perform operations including combining the photos into a panorama of the at least one location, and where the logic when executed is further operable to perform operations including iteratively replacing photos in the panorama with replacement photos based on one or more predetermined replacement criteria. In some implementations, to generate the model, the logic when executed is further operable to perform operations including combining the photos into a panorama of the at least one location, where the logic when executed is further operable to perform operations including iteratively replacing photos in the panorama with replacement photos based on one or more predetermined replacement criteria, and where the replacement criteria may include one or more of quality and resolution.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example network environment, which may be used to implement the implementations described herein.

FIG. 2 illustrates an example simplified flow diagram for generating static scenes, according to some implementations.

FIGS. 3A, 3B, 3C, and 3D illustrate example simplified photos of objects from different vantage points.

FIG. 4 illustrates an example diagram of an eye tracking device that tracks the gaze of a user, according to some implementations.

FIG. 5 illustrates an example simplified flow diagram for generating an attention map, according to some implementations.

FIG. 6 illustrates an example diagram of an attention map, according to some implementations.

FIG. 7 illustrates an example simplified flow diagram for generating a model of a location, according to some implementations.

FIG. 8 illustrates an example simplified photo composite, according to some implementations.

FIG. 9 illustrates an example simplified model of a location, according to some implementations.

FIG. 10 illustrates a block diagram of an example server device, which may be used to implement the implementations described herein.

DETAILED DESCRIPTION

Implementations described herein facilitate the generating of static scenes. In various implementations, a system collects photos associated with objects in at least one location, where one or more of the photos are captured using one or more respective devices that are operable to track a gaze of a user. The system then collects attention information associated with one or more of the objects, where the attention information is based on tracking the gazes of one or more users. For example, the system may determine which objects at the location are receiving attention from the users. The system then generates an attention map based on the attention information.

The system then generates a static scene of a location (also referred to as a model of the location) based on the photos and the attention map. In some implementations, to generate the model, the system may identify the objects in the attention map; select photos for each of the objects based on one or more predetermined selection criteria; and then combine the photos into a panorama of the location. In some implementations, to generate the model, the system may register images of the objects in the photos; calibrate the images of the objects; and then blend the images. In some implementations, the system may iteratively replace photos in the panorama with replacement photos based on one or more predetermined replacement criteria. The replacement criteria may include, for example, one or more of quality and resolution. Such a replacement process improves the model of the location over time.

FIG. 1 illustrates a block diagram of an example network environment 100, which may be used to implement the implementations described herein. In some implementations, network environment 100 includes a system 102, which includes a server device 104 and a social network database 106. The term system 102 and phrase “social network system” may be used interchangeably. Network environment 100 also includes client devices 110, 120, 130, and 140, which may communicate with each other via system 102 and a network 150.

For ease of illustration, FIG. 1 shows one block for each of system 102, server device 104, and social network database 106, and shows four blocks for client devices 110, 120, 130, and 140. Blocks 102, 104, and 106 may represent multiple systems, server devices, and social network databases. Also, there may be any number of client devices. In other implementations, network environment 100 may not have all of the components shown and/or may have other elements including other types of elements instead of, or in addition to, those shown herein.

In various implementations, users U1, U2, U3, and U4 may communicate with each other using respective client devices 110, 120, 130, and 140. Users U1, U2, U3, and U4 may also use respective client devices 110, 120, 130, and 140 to take photos. In various implementations, client devices 110, 120, 130, and 140 may include any types of electronic devices such as mobile phones (e.g., smart phones), tablets, notebook computers, desktop computers, digital cameras, etc. Such client devices 110, 120, 130, and 140 that are not dedicated digital cameras may include integrated digital cameras.

In various implementations, system 102 may utilize an eye tracking device for collecting attention information, where the eye tracking device may be used in conjunction with a camera device, which may be a dedicated digital camera or a digital camera integrated with an electronic device (e.g., any of client devices 110, 120, 130, 140, etc.). The eye tracking device may itself be integrated with any one or more of client devices 110, 120, 130, 140, etc. As described in more detail below, such an eye tracking device may be any suitable eye tracking device that measures eye positions such as the point of gaze (e.g., the user's line of sight) and/or measures eye movement.

In some implementations, client devices 110, 120, 130, and 140 may include wearable computing devices, including any hands-free devices. For example, in some implementations, one or more client devices may include devices that operate with a head-mounted camera, head-mounted eye tracking device, and/or head-mounted display.

FIG. 2 illustrates an example simplified flow diagram for generating static scenes, according to some implementations. Referring to both FIGS. 1 and 2, a method is initiated in block 202, where system 102 collects photos associated with objects in at least one location. In various implementations, system 102 collects the photos from multiple users. For example, system 102 may collect photos of the same object from many different people, from many different vantage points.

FIGS. 3A, 3B, 3C, and 3D illustrate example simplified photos of objects in a location. FIG. 3A shows a tree 302. FIG. 3B shows tree 302 and a pyramid 304. FIG. 3B also shows a portion of a pyramid 306. FIG. 3C shows pyramid 304 and a portion of pyramid 306. FIG. 3D shows pyramids 304 and 306. For ease of illustration, FIGS. 3A, 3B, 3C, and 3D show three objects from different vantages points. A given location or scene may have many more objects, depending on the particular scenario. Furthermore, system 102 may collect numerous photos of a given object from many different vantage points.

In block 204, system 102 collects attention information associated with one or more of the objects. The one or more objects may be set in any given visual context. For example, a given object may be a pyramid in a desert setting, as shown in the examples of FIGS. 3A, 3B, 3C, and 3D. Other examples may include a buildings in a city, elements in a natural landscape, etc.

In various implementations, attention information characterizes the attention (e.g., focus, fixation points, etc.) of a given user with respect to a particular object during any given point in time. In various implementations, attention information may include any combination of attention values, attention parameter values, and gaze information, which are described in more detail below.

In some implementations, gaze information may include various gaze parameter values associated with pitch, yaw, roll, line of sight, field of view, etc. In various implementations, one or more of the photos are captured using one or more respective devices that are operable to track the gaze (e.g., line of sight) of a user. Devices that are operable to track a gaze facilitate system 102 in determining values of gaze parameters such as pitch, yaw, roll, line of sight, field of view, etc. For example, a given device while capturing photos may also capture gaze information. Such an eye tracking device may be any suitable eye tracking device that measures the point of gaze (e.g., the user's line of sight). Example implementations of a device that is operable to track the gaze of a user are described in more detail below in connection with FIG. 4.

For ease of illustration, some implementations are described herein in the context of the gaze of a single user. These implementations and others also apply to gazes of multiple users. For example, for a given object (e.g., a pyramid), system 102 may track, log, and aggregate the gaze information of multiple users. As such, in some implementations, attention information is based on tracking gazes of one or more users. For example, system 102 may determine which objects at the location are receiving attention from the users. Example implementations involving attention information are described in detail below in connection with FIG. 5.

In block 206, system 102 generates an attention map based on the attention information. The attention map shows how much attention particular objects have received relative to other objects. In various implementations, the attention map is based on various aspects of attention information. For example, the attention map may be based on tracking gazes of one or more of the users relative to one or more objects (e.g., gaze information), and/or based on any other aspect of attention information such as attention values, attention parameter values, etc.). Example implementations directed to generating an attention map and attention information are described in more detail below in connection with FIG. 5.

In block 208, system 102 generates a model of the location based on the photos and the attention map. Note that the phrases “model of a location” and “static scene” may be used interchangeably. In various implementations, system 102 generates the model of the location by combining the photos into a panorama of the location. Various implementations directed to generating a model of a location are described in detail below in connection with FIG. 7.

While some implementations are described herein in the context of one location, other implementations also apply to multiple locations.

FIG. 4 illustrates an example diagram of an eye tracking device 400 that tracks the gaze of a user, according to some implementations. In some implementations, eye tracking device 300 may be positioned in the head area of the user. For example, as shown, eye tracking device 300 may be positioned relatively close to an eye of the user.

In various implementations, eye tracking device 400 may use suitable eye tracking technologies, including any suitable eye-tracking hardware components and algorithms, to measure eye positions and eye movement. For example, eye tracking device 400 may use any suitable eye tracking technologies to measure the gaze of the user (e.g., the user's line of sight) or the motion of an eye relative to the head. For example, eye tracking device 400 may use a laser and laser technology to measure eye positions and eye movement relative to objects in the environment.

In some implementations, eye tracking device 400 may track the gaze of the user by tracking one or more parameters such as pitch, yaw, roll, line of sight, field of view, etc. FIG. 4 shows a pitch axis, yaw axis, and roll axis to illustrate how eye tracking device 400 may move depending on eye movement and/or head movement of the user, as the attention that the user places on particular objects would influence and correlate to both eye movement and head movement of the user.

FIG. 4 shows example objects 302, 304, and 306, where object 302 is a tree, object 304 is a large pyramid, and object 306 is a small pyramid. As shown, objects 302, 304, and 306 are in user's line of site 408 and in the user's field of view 410. For ease of illustration, FIG. 4 shows three objects 302, 304, and 306. Any given scene in the user's field of view 410 may include any number of objects, including people.

In some implementations, system 102 may receive gaze information (e.g., parameter values associated with tracked parameters) directly from eye tracking device 400 or from any other one or more suitable storage locations. For example, in some implementations, eye tracking device 400 may send gaze information to system 102 as the user gazes at particular objects. In some implementations, when used with a camera device, eye tracking device 400 may send gaze information to system 102 as a camera device sends photos to system 102 (e.g., as the photos are captured). In some implementations, eye tracking device 400 may store gaze information local to the user's client device (e.g., if used with a dedicated digital camera, or if used with a mobile phone or other electronic device that has an integrated digital camera, etc.).

FIG. 5 illustrates an example simplified flow diagram for generating an attention map, according to some implementations. Referring to FIGS. 1, 4, and 5, a method is initiated in block 502, where system 102 receives gaze information. In some implementations, the gaze information associated with a given user may also be referred to as the gaze pattern of the user, or the gaze of the user. As indicated above, the gaze information may include one or more parameter values associated with gaze parameters such as pitch, yaw, roll, line of sight, field of view, etc.

In some implementations, where photos are provided by users and where the photos are associated with the gaze information, system 102 may also associate geotags with both the photos and gaze information. System 102 may include such geotags in the gaze parameters. In some implementations, where photos are not provided, system 102 may include geographical coordinates from gaze information, and include such geographical coordinates with the gaze parameters

In block 504, system 102 identifies objects based on the gaze information. In some implementations, system 102 may receive one or more photos of one or more objects. System 102 may then identify objects in each photo via any suitable object identification algorithm.

In some implementations, system 102 may also recognize the identified objects. For example, after system 102 identifies an object such as a monument or face, system 102 may then apply a suitable recognition algorithm to recognize an identity associated with the particular object (e.g., monument or particular person). Example implementations directed to object recognition are described in more detail below.

In some implementations, system 102 may receive gaze information independently of photos being captured. In some implementations, system 102 may also identify a given object based on the gaze information even before the user captures a photo of the object or even if the user does not ultimately capture a photo of the object. Accordingly, system 102 may capture gaze information even if the user is not concurrently capturing photos. In various implementations, system 102 may track only gaze information without recognizing which user is providing the gaze information. In other words, gaze information may be anonymous. In some implementations, a given user may want system 102 to recognizing the given user as the user providing the gaze information in order to enable system 102 in providing individual and/or customized services for the user. As described in more detail below, system 102 enables users to specify and/or consent to the use of personal information, including gaze information.

In block 506, system 102 associates the gaze information with each of the one or more objects receiving the attention of the user. For example, in some implementations, the gaze information (e.g., the combination of gaze parameter values) characterizes the gaze of the user. System 102 may determine, from the gaze information, fixation points on one or more objects in a given photo, including fixation points at particular portions of such objects.

Note that a photo need not necessarily be provided by the user associated with the gaze information. System 102 may determine an appropriate photo based on geolocation information, other photos provided by the user associated with the gaze information, etc. In some implementations, where system 102 aggregates gazes from multiple users, system 102 may associate the gaze information from the different users with the same one or more objects. For example, referring to FIG. 4, if multiple users gaze at object 304, system 102 may associate the gaze information associated with all of such users with the same object 304.

In block 508, system 102 determines an attention value for each of the one or more objects based on the gaze information. In some implementations, the attention value may be based on one or more attention parameter values. Such attention parameters may include, for example, the amount of time a given user gazed at a given object. In some implementations, such attention parameters have corresponding attention subvalues that system 102 may aggregate in order to derive a given attention value. In various implementations, the phrase “attention parameter value” may be use interchangeably with the phrase “attention subvalue.”

In some implementations, system 102 may assign an attention subvalue that is proportional to the total amount of time that the user gazed at the object. For example, system 102 may assign a higher attention subvalue if the user gazed at the object for 10 minutes versus only 2 minutes.

In some implementations, system 102 may assign an attention subvalue that is proportional to the total number of times that the user gazed at a given object. For example, system 102 may assign a higher attention subvalue if the user gazed at the object 5 different times versus a single time.

In some implementations, system 102 may assign an attention subvalue that is proportional to the total size and/or percentage of a given object at which the user gazed. For example, system 102 may assign a higher attention subvalue if the user gazed at 75% of the object versus 25% of the object.

In some implementations, system 102 may assign an attention subvalue that is proportional to the total number of people who gazed at a given object. For example, system 102 may assign a higher attention subvalue if 1,000 people gazed at the object versus 5 people. Other attention parameters are possible, and the particular number of attention parameters and the types of parameters will depend on the particular implementation.

In block 510, system 102 generates an attention map based on the attention values. As indicated above, the attention map may be based on attention information in that the attention information may in turn be based on attention values. An example attention map is described in more detail below in connection with FIG. 6.

FIG. 6 illustrates an example diagram of an attention map 600, according to some implementations. As indicated above, an attention map shows how much attention particular objects have received relative to other objects. As shown, objects 302, 304, and 306 are shown with Xs overlaid, where the number of Xs is proportional to the attention value. In some implementations, objects 302, 304, and 306 may each be shown with an actual attention value. For example, as shown in this example implementation, object 302 has an attention value of 9, object 304 has an attention value of 97, and object 306 has an attention value of 85. The range of attention values may vary (e.g., 0 to 1.0; 0 to 100; 1 to 1,000, etc.), and the particular range and/or numbering scheme will depend on the particular implementation.

In some implementations, system 102 may assign a color to each object, where the particular color may correspond to the size of the attention value. For example, an object associated with yellow may have a relatively higher attention value than an object associated with blue; an object associated with orange may have a relatively higher attention value than an object associated with yellow; an object associated with red may have a relatively higher attention value than an object associated with orange. The particular color scheme will vary, depending on the particular implementation.

FIG. 7 illustrates an example simplified flow diagram for generating a model of a location, according to some implementations. As shown, a method is initiated in block 702, where system 102 identifies the objects in the attention map. For example, system 102 may identify objects (e.g., monuments, buildings, trees, etc.) and/or portions of objects that are receiving attention from people (e.g., receiving high attention from tourists).

In block 704, system 102 selects photos for each of the objects based on one or more predetermined selection criteria. For example, in various implementations, the one or more predetermined selection criteria may be based on resolution, quality, field of view, level of attention, etc.

With regard to resolution, system 102 may rank photos of a particular object based on resolution, and then select photos that meet a predetermined resolution threshold. With regard to quality, system 102 may rank photos of a particular object based on quality, and then select photos that meet a predetermined quality threshold. With regard to field of view, system 102 may rank photos of a particular object based on field of view, and then select photos that meet a predetermined field of view threshold.

With regard to level of attention, system 102 may search a corpus of photos for photos that include objects that match objects identified in the attention map. For example, referring to attention map 600 of FIG. 6, system 102 may identify pyramid 304 as receiving a significant amount of attention. System 102 may then search a database for photos of pyramid 304. Similarly, system 102 may search the database for photos of objects that correspond to other objects receiving attention according to attention map 600.

With further regard to level of attention, system 102 may apply different standard levels to particular criteria (e.g., resolution, quality, field of view, etc.) based on level of attention. For example, system 102 may apply a higher standard to objects that receive a higher level of attention (e.g., more popular objects such as monuments). Conversely, system 102 may apply a lower standard to objects that receive a lower level of attention (e.g., less popular objects such as lawns, clear skies, etc.).

In some implementations, to determine a standard level for photos of a given object, system 102 may determine one or more attention thresholds, where system 102 may associate predetermined attention thresholds proportionally with levels of attention (e.g., levels of popularity) among users. For example, system 102 may associate a relatively lower attention threshold with a relatively lower level of attention. Conversely, system 102 may associate a relatively higher attention threshold with a relatively higher level of attention. The actual number of predetermined thresholds may vary, and will depend on the particular implementation.

In some implementations, system 102 may determine an attention value for each of the one or more objects; compare the attention value for each object against one or more predetermined attention thresholds; and then determine if the attention value for each object meets one or more of the predetermined attention thresholds. In some implementations, an attention value meets a given attention threshold if the attention value is greater than the attention threshold. In some implementations, an attention value meets a given attention threshold if the attention value is greater than or equal to the attention threshold. Also, a given attention value may meet multiple attention thresholds. In some implementations, system 102 may associate the attention value with the highest attention threshold met for the purposes of grouping photos for sharing among users. The particular criteria for meeting an attention threshold may vary and will depend on the particular implementation.

While criteria based on resolution, quality, field of view, level of attention, are described, other criteria are also possible, depending on the specific implementation. For example, predetermined selection criteria may be based on geolocation. In some implementations, system 102 may ensure that the geolocation of a particular object (e.g., pyramid 304) matches and/or corresponds to the geotag associated with retrieved photos of that object.

In block 706, system 102 combines the photos into a panorama of the location. In some implementations, system 102 combines photos by registering images of the objects in the photos; calibrating the images of the objects; and then blending the images.

FIG. 8 illustrates an example simplified photo composite 800, according to some implementations. As shown, photo composite 800 includes a combination of photos 802, 804, 806, and 808, which may correspond to the photos of FIGS. 3A, 3B, 3C, and 3D. Photo composite 800 shows tree 302, pyramid 304, and pyramid 306. As shown, the photos are registered and calibrated into photo composite 800.

FIG. 9 illustrates an example simplified model 900 of a location, according to some implementations. As shown, the photos are blended into a single model 900 of the location. As shown, model 900 includes tree 302, pyramid 304, and pyramid 306, which were originally from different photos.

For ease of illustration, model 900 of FIG. 9 shows a simple panoramic view. Depending on the number of objects captured, photos available, points of view, etc., models of locations may provide various types panoramas of varying complexities (e.g., 360 degree panoramas, cylindrical panoramas, spherical panoramas, cubical panoramas, etc.). The specific panoramas provided by a given model of a location will depend on the specific implementation.

In block 708, system 102 iteratively replaces photos in the panorama with replacement photos based on one or more predetermined replacement criteria. In various implementations, the one or more predetermined replacement criteria may be the same or similar to the predetermined selection criteria described above. For example, in various implementations, the one or more predetermined selection criteria may be based on resolution, quality, field of view, level of attention, geolocation, etc.

In various implementations, system 102 selects replacement photos continuously in order to improve a given model of a location. In many situations, numerous photos of the same object are substantially the same, yet slight variations of each other. Such variations in photos may be due to gaze differences (e.g., differences in height among people, etc.), camera differences (e.g., differences in quality, settings, etc.), lighting differences (e.g., differences in time of day, weather, etc.), background and environmental differences (e.g., variations in the sky, clouds, airplanes, etc.), foreground differences (e.g., people, friends, family, etc.), etc. In various implementations, system 102 factors in such differences when initially selecting photos for a given model of a location. Similarly, system 102 factors in such differences when replacing photos of the model. Such a replacement process improves the model of the location over time.

In various implementations, by utilizing an attention map for selecting photos for objects, system 102 allocates appropriate resources for building and refining a given model of a location. In other words, system 102 concentrates various resources on object of a model of a given location that are most meaningful (e.g., receives the most attention from people).

In various implementations, system 102 may cause the generated model of a given location to be displayed on a display screen and/or navigator screen to enable a user to view the model.

In some implementations, system 102 enables a user manually modify and improve a given model. For example, in some implementations, system 102 enables a user to overlay one or more user-selected photos over one or more portions of the panorama. This allows one or more users to improve a given model of a location as desired.

Models provided by implementations described herein may have various applications. For example, in some implementations, a model may provide a high-resolution texture map for users. In some implementations, a model may enable a user to enhance, augment, or improve new photos captured. In some implementations, a model may guide a user to different aspects of a location, which may inform the user of different photo opportunities. Such photo opportunities may benefit the user when capturing new photos in that the user may notice objects via a model that the user might not otherwise notice. Such photo opportunities may also improve a given model and/or benefit a community of people in that users may contribute more photos to be incorporated into the given model.

Although the steps, operations, or computations described herein may be presented in a specific order, the order may be changed in particular implementations. Other orderings of the steps are possible, depending on the particular implementation. In some particular implementations, multiple steps shown as sequential in this specification may be performed at the same time. Also, some implementations may not have all of the steps shown and/or may have other steps instead of, or in addition to, those shown herein.

While system 102 is described as performing the steps as described in the implementations herein, any suitable component or combination of components of system 102 or any suitable processor or processors associated with system 102 may perform the steps described.

In various implementations, system 102 may utilize a variety of recognition algorithms to recognize faces, landmarks, objects, etc. in photos. Such recognition algorithms may be integral to system 102. System 102 may also access recognition algorithms provided by software that is external to system 102 and that system 102 accesses.

In various implementations, system 102 enables users to specify and/or consent to the use of personal information. Use of personal information may include, for example, system 102 using their faces in photos or using their identity information in recognizing people identified in photos, and may also include system 102 tracking the gaze of a user even when the user is not capturing photos. In some implementations, system 102 may provide users with multiple selections directed to specifying and/or consenting to the use of personal information. For example, selections with regard to specifying and/or consenting may be associated with individual photos, all photos, individual photo albums, all photo albums, etc. The selections may be implemented in a variety of ways. For example, system 102 may cause buttons or check boxes to be displayed next to various selections. In some implementations, system 102 enables users to specify and/or consent to the use of using their photos for facial recognition in general. Example implementations for recognizing faces and other objects are described in more detail below.

In various implementations, system 102 obtains reference images of users, where each reference image includes an image of a face that is associated with a known user. The user is known, in that system 102 has the user's identity information such as the user's name and other profile information. In some implementations, a reference image may be, for example, a profile image that the user has uploaded. In some implementations, a reference image may be based on a composite of a group of reference images.

In some implementations, to recognize a face in a photo, system 102 may compare the face (i.e., image of the face) and match the face to reference images of users. Note that the term “face” and the phrase “image of the face” are used interchangeably. For ease of illustration, the recognition of one face is described in some of the example implementations described herein. These implementations may also apply to each face of multiple faces to be recognized.

In some implementations, system 102 may search reference images in order to identify any one or more reference images that are similar to the face in the photo. In some implementations, for a given reference image, system 102 may extract features from the image of the face in a photo for analysis, and then compare those features to those of one or more reference images. For example, system 102 may analyze the relative position, size, and/or shape of facial features such as eyes, nose, cheekbones, mouth, jaw, etc. In some implementations, system 102 may use data gathered from the analysis to match the face in the photo to one more reference images with matching or similar features. In some implementations, system 102 may normalize multiple reference images, and compress face data from those images into a composite representation having information (e.g., facial feature data), and then compare the face in the photo to the composite representation for facial recognition.

In some scenarios, the face in the photo may be similar to multiple reference images associated with the same user. As such, there would be a high probability that the person associated with the face in the photo is the same person associated with the reference images.

In some scenarios, the face in the photo may be similar to multiple reference images associated with different users. As such, there would be a moderately high yet decreased probability that the person in the photo matches any given person associated with the reference images. To handle such a situation, system 102 may use various types of facial recognition algorithms to narrow the possibilities, ideally down to one best candidate.

For example, in some implementations, to facilitate in facial recognition, system 102 may use geometric facial recognition algorithms, which are based on feature discrimination. System 102 may also use photometric algorithms, which are based on a statistical approach that distills a facial feature into values for comparison. A combination of the geometric and photometric approaches could also be used when comparing the face in the photo to one or more references.

Other facial recognition algorithms may be used. For example, system 102 may use facial recognition algorithms that use one or more of principal component analysis, linear discriminate analysis, elastic bunch graph matching, hidden Markov models, and dynamic link matching. It will be appreciated that system 102 may use other known or later developed facial recognition algorithms, techniques, and/or systems.

In some implementations, system 102 may generate an output indicating a likelihood (or probability) that the face in the photo matches a given reference image. In some implementations, the output may be represented as a metric (or numerical value) such as a percentage associated with the confidence that the face in the photo matches a given reference image. For example, a value of 1.0 may represent 100% confidence of a match. This could occur, for example, when compared images are identical or nearly identical. The value could be lower, for example 0.5 when there is a 50% chance of a match. Other types of outputs are possible. For example, in some implementations, the output may be a confidence score for matching.

For ease of illustration, some example implementations described herein have been described in the context of a facial recognition algorithm. Other similar recognition algorithms and/or visual search systems may be used to recognize objects such as landmarks, logos, entities, events, etc. in order to implement implementations described herein.

Implementations described herein provide various benefits. For example, implementations described herein synthesize static scenes based on photos captured by different people. Implementations continually improve such static scenes as different people contribute new photos to the scenes.

FIG. 10 illustrates a block diagram of an example server device 1000, which may be used to implement the implementations described herein. For example, server device 1000 may be used to implement server device 104 of FIG. 1, as well as to perform the method implementations described herein. In some implementations, server device 1000 includes a processor 1002, an operating system 1004, a memory 1006, and an input/output (I/O) interface 1008. Server device 1000 also includes a social network engine 1010 and a media application 1012, which may be stored in memory 1006 or on any other suitable storage location or computer-readable medium. Media application 1012 provides instructions that enable processor 1002 to perform the functions described herein and other functions.

For ease of illustration, FIG. 10 shows one block for each of processor 1002, operating system 1004, memory 1006, I/O interface 1008, social network engine 1010, and media application 1012. These blocks 1002, 1004, 1006, 1008, 1010, and 1012 may represent multiple processors, operating systems, memories, I/O interfaces, social network engines, and media applications. In other implementations, server device 1000 may not have all of the components shown and/or may have other elements including other types of elements instead of, or in addition to, those shown herein.

Although the description has been described with respect to particular embodiments thereof, these particular embodiments are merely illustrative, and not restrictive. Concepts illustrated in the examples may be applied to other examples and implementations.

Note that the functional blocks, methods, devices, and systems described in the present disclosure may be integrated or divided into different combinations of systems, devices, and functional blocks as would be known to those skilled in the art.

Any suitable programming languages and programming techniques may be used to implement the routines of particular embodiments. Different programming techniques may be employed such as procedural or object-oriented. The routines may execute on a single processing device or multiple processors. Although the steps, operations, or computations may be presented in a specific order, the order may be changed in different particular embodiments. In some particular embodiments, multiple steps shown as sequential in this specification may be performed at the same time.

A “processor” includes any suitable hardware and/or software system, mechanism or component that processes data, signals or other information. A processor may include a system with a general-purpose central processing unit, multiple processing units, dedicated circuitry for achieving functionality, or other systems. Processing need not be limited to a geographic location, or have temporal limitations. For example, a processor may perform its functions in “real-time,” “offline,” in a “batch mode,” etc. Portions of processing may be performed at different times and at different locations, by different (or the same) processing systems. A computer may be any processor in communication with a memory. The memory may be any suitable processor-readable storage medium, such as random-access memory (RAM), read-only memory (ROM), magnetic or optical disk, or other tangible media suitable for storing instructions for execution by the processor. 

What is claimed is:
 1. A computer-implemented method comprising: collecting photos associated with objects in at least one location, wherein one or more of the photos are captured using one or more respective devices that are operable to track a gaze of a user; collecting attention information associated with one or more of the objects, wherein the attention information is based on tracking gazes of one or more users and based on one or more fixation points of one or more objects in the least one location; generating an attention map based on the attention information; associating a color with each object in the at least one location, wherein each color corresponds to a size of an attention value; and generating a model of the at least one location based on the photos and the attention map, wherein the generating of the model comprises combining the photos into a panorama of the at least one location based on the one or more fixation points, and wherein the method further comprises iteratively replacing one or more photos in the panorama with replacement photos based on one or more predetermined replacement criteria.
 2. A computer-implemented method comprising: collecting photos associated with objects in at least one location; collecting attention information associated with one or more of the objects, wherein the attention information is based on tracking gazes of one or more users and based on one or more fixation points of one or more objects in the least one location; generating an attention map based on the attention information; associating a color with each object in the at least one location, wherein each color corresponds to a size of an attention value; and generating a model of the at least one location based on the photos and the attention map, wherein the generating of the model comprises combining the photos into a panorama of the at least one location based on the one or more fixation points.
 3. The method of claim 2, wherein one or more of the photos are captured using one or more respective devices that are operable to track a gaze of a user.
 4. The method of claim 2, wherein the generating of the model comprises: identifying the objects in the attention map; selecting photos for each of the objects based on one or more predetermined selection criteria; and combining the photos into the panorama of the at least one location.
 5. The method of claim 2, wherein the generating of the model comprises: identifying the objects in the attention map; selecting photos for each of the objects based on one or more predetermined selection criteria, wherein the one or more predetermined selection criteria is based on one or more of resolution, quality, field of view, and level of attention; and combining the photos into the panorama of the at least one location.
 6. The method of claim 2, wherein the method further comprises iteratively replacing one or more photos in the panorama with replacement photos based on one or more predetermined replacement criteria, and wherein the replacement criteria may include one or more of quality and resolution.
 7. The method of claim 2, wherein the method further comprises enabling a user to overlay one or more user-selected photos over one or more portions of the panorama.
 8. A system comprising: one or more processors; and logic encoded in one or more tangible media for execution by the one or more processors and when executed operable to perform operations comprising: collecting photos associated with objects in at least one location; collecting attention information associated with one or more of the objects, wherein the attention information is based on tracking gazes of one or more users and based on one or more fixation points of one or more objects in the least one location; generating an attention map based on the attention information; associating a color with each object in the at least one location, wherein each color corresponds to a size of an attention value; and generating a model of the at least one location based on the photos and the attention map, wherein the generating of the model comprises combining the photos into a panorama of the at least one location based on the one or more fixation points.
 9. The system of claim 8, wherein one or more of the photos are captured using one or more respective devices that are operable to track a gaze of a user.
 10. The system of claim 8, wherein the attention information is based on tracking gazes of one or more users.
 11. The system of claim 8, wherein, to generate the model, the logic when executed is further operable to perform operations comprising: identifying the objects in the attention map; selecting photos for each of the objects based on one or more predetermined selection criteria; and combining the photos into the panorama of the at least one location.
 12. The system of claim 8, wherein, to generate the model, the logic when executed is further operable to perform operations comprising: identifying the objects in the attention map; selecting photos for each of the objects based on one or more predetermined selection criteria, wherein the one or more predetermined selection criteria is based on one or more of resolution, quality, field of view, and level of attention; and combining the photos into the panorama of the at least one location.
 13. The system of claim 8, wherein the logic when executed is further operable to perform operations comprising iteratively replacing one or more photos in the panorama with replacement photos based on one or more predetermined replacement criteria.
 14. The system of claim 8, wherein the logic when executed is further operable to perform operations comprising iteratively replacing one or more photos in the panorama with replacement photos based on one or more predetermined replacement criteria, and wherein the replacement criteria may include one or more of quality and resolution. 